解决多视图聚类中缺失数据问题，提升聚类准确性
Mask-informed Deep Contrastive Incomplete Multi-view Clustering
- 用掩码机制融合不完整多视图数据，识别共性表示
- 引入先验知识增强对比学习，提升表示能力
- 在多种数据集上表现优于现有方法，适合缺失数据场景
多视图聚类（MvC）利用多个视图的信息揭示数据潜在结构。尽管MvC已取得显著进展，但特定视图中缺失样本对跨视图知识融合的影响仍是关键挑战。本文提出一种新型掩码感知深度对比不完整多视图聚类（Mask-IMvC）方法，巧妙构建视图共性表示用于聚类。具体而言，设计掩码感知融合网络，在考虑样本在不同视图中观测状态作为掩码的前提下，聚合不完整多视图信息，从而降低缺失值的负面影响。此外，设计先验知识辅助的对比学习损失，通过注入来自不同视图的样本邻域信息，增强聚合后的视图共性表示能力。大量实验表明，所提方法在多个标准MvC数据集上，无论在完整或不完整场景下，均优于当前最先进方法。
原文摘要 · Abstract (English)
Multi-view clustering (MvC) utilizes information from multiple views to uncover the underlying structures of data. Despite significant advancements in MvC, mitigating the impact of missing samples in specific views on the integration of knowledge from different views remains a critical challenge. This paper proposes a novel Mask-informed Deep Contrastive Incomplete Multi-view Clustering (Mask-IMvC) method, which elegantly identifies a view-common representation for clustering. Specifically, we introduce a mask-informed fusion network that aggregates incomplete multi-view information while considering the observation status of samples across various views as a mask, thereby reducing the adverse effects of missing values. Additionally, we design a prior knowledge-assisted contrastive learning loss that boosts the representation capability of the aggregated view-common representation by injecting neighborhood information of samples from different views. Finally, extensive experiments are conducted to demonstrate the superiority of the proposed Mask-IMvC method over state-of-the-art approaches across multiple MvC datasets, both in complete and incomplete scenarios.
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